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Claude Now Available on Azure Foundry, but Restricted in Europe

by Chief Editor
written by Chief Editor

Anthropic’s Claude models are now generally available via Microsoft Azure Foundry, allowing customers to access Claude 3.5 Sonnet, 3.5 Haiku, and 3.0 Opus using existing Azure billing and governance. While this integration streamlines procurement for US-based enterprises, it does not currently provide guaranteed data residency in Europe, according to Microsoft and Anthropic documentation.

Why does the European data residency gap matter?

The primary friction point for European enterprises is that Claude models on Foundry function as third-party marketplace offerings rather than first-party Azure services. According to Gregor Beuster, a Member of Technical Staff, this distinction is critical because OpenAI models on Azure operate within the Azure trust boundary, whereas Anthropic remains an independent data processor.

Practitioners point out that even when “hosted on Azure,” the architecture relies on “Global Standard” deployment. As noted by Reddit user Radubogdan, this means inference can be routed to US infrastructure regardless of the endpoint address. Because Anthropic is a US-based company, the US CLOUD Act applies, and the company’s documentation confirms that automatic safeguards may trigger an Anthropic Trust and Safety review, potentially moving data outside the Azure boundary.

Did you know?
Anthropic’s regional compliance page lists the availability of Microsoft Foundry in Europe as “Coming 2026,” but has provided no specific month or quarter for this rollout.

How do procurement barriers compare to technical limitations?

For many IT leaders, the Foundry launch is a practical solution to vendor onboarding hurdles. By drawing down existing Microsoft Azure Consumption Commitments (MACC), teams can bypass the need to open new vendor relationships, a benefit highlighted by enterprise users on LinkedIn. However, this ease of billing is currently offset by capacity constraints.

How do procurement barriers compare to technical limitations?

Jannik Reinhard, a Microsoft MVP, noted that the “Generally Available” label does not guarantee immediate access. Users must frequently submit request forms and wait for approval, which Reinhard argues falls short of a “professional service” standard. Karl Wirén echoed this sentiment, noting that the need for manual approval processes remains a hurdle for production-grade deployments.

What are the alternatives for strict compliance environments?

European organizations with strict data residency requirements, such as financial or healthcare institutions, currently face a limited set of options. While Anthropic’s documentation provides clear data residency guarantees for deployments via Amazon Web Services (AWS) Bedrock or Google Cloud Vertex AI, those same guarantees do not currently extend to the Microsoft Foundry environment.

How to Configure Claude Code with Azure AI Foundry | Step-by-Step Tutorial

As Alistair Doran, Head of Digital Product Management at BDO, observed, the lack of European or UK-region availability is a significant barrier for organizations that require data to stay within specific sovereign boundaries. Until Microsoft and Anthropic provide a roadmap for guaranteed EU-hosted inference, many European architects remain restricted to using Azure OpenAI, where first-party status ensures data does not leave the designated EU zone.

Frequently Asked Questions

  • Is Claude on Azure Foundry GDPR compliant? While the integration follows Azure’s governance frameworks, the lack of EU-based inference means data may be processed in the US, which presents compliance challenges for organizations requiring local data sovereignty.
  • Can I guarantee my data stays in Europe when using Claude on Foundry? No. Current documentation specifies “Global Standard” deployment, meaning inference can be routed to US-based infrastructure.
  • Do existing MACC credits apply to Claude on Foundry? Yes, usage of Claude models through the Azure Foundry marketplace draws down against existing Microsoft Azure Consumption Commitments.
  • When will EU-hosted inference be available? Anthropic has indicated that Microsoft Foundry in Europe is expected to arrive in 2026, though no specific date has been confirmed.
Pro Tip: Before committing to a large-scale deployment, verify your organization’s specific data residency requirements against the “Global Standard” deployment model, as the current Foundry setup does not mirror the regional guarantees of AWS Bedrock or Google Vertex AI.

Are you navigating the challenges of AI procurement in a regulated industry? Share your experiences with model deployment in the comments below.

Frequently Asked Questions
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News

Marcos Pushes for Philippines-Canada Digital Economy Partnership

by Rachel Morgan News Editor
written by Rachel Morgan News Editor

President Ferdinand Marcos Jr. announced on Friday that the Philippines is ready to advance the digital economy through partnership with Canada. During a roundtable in Vancouver with executives from the information technology-business process management (IT-BPM) sector, the President emphasized that the upcoming Philippine-Canada Free Trade Agreement aims to usher in more bilateral trade, strategic investments, and seamless economic cooperation.

Shifting the Role of the IT-BPM Sector

President Marcos stated that the IT-BPM sector was no longer a back office function as it is now a core driver of innovation, productivity and global value creation. According to the President, core business operations—including cybersecurity, analytics, financial service support, and healthcare information management—are now central to how global enterprises compete and how they grow.

Shifting the Role of the IT-BPM Sector

Canadian firms already maintain a strong participation in this landscape. These companies provide support in areas such as software as a service, enterprise software, consulting, and next-generation digital operations. Among the organizations represented during the discussions were Blackberry, CGI, Everise, ManuLife, OpenText, NQX, Sun Life, Telus Corp., and InTouchCX.

Did You Know?

There are more than 24,000 Filipinos already driving AI, digital transformation and other high-value services for global clients.

Future Implications for the Filipino Workforce

The government intends to leverage this partnership to prepare the local workforce for the industries of tomorrow. President Marcos noted that discussions with Canadian technology leaders specifically targeted the expansion of opportunities in artificial intelligence (AI), software engineering, and digital innovation. By investing in the skills of our people, the administration aims to position Filipino talent to compete and succeed on the global stage.

Expert Insight:

The strategic pivot toward high-value services like AI and cybersecurity is supported by the government’s engagement with Canadian companies.

What Happens Next

The ongoing engagement with Canadian technology firms suggests a shift toward more specialized, high-value outsourcing. President Marcos has encouraged Telus to invest in the Philippines. Future developments will likely depend on the finalization of the trade agreement, which aims to catalyze a robust digital economy.

President Ferdinand Marcos Jr. Arrives in Vancouver for Official Canada Visit

Frequently Asked Questions

What is the goal of the Philippine-Canada Free Trade Agreement?
The agreement aims to catalyze a robust digital economy by increasing bilateral trade, fostering strategic investments, and improving seamless economic cooperation.

Which Canadian companies were involved in the discussions?
The companies represented included Blackberry, CGI, Everise, ManuLife, OpenText, NQX, Sun Life, Telus Corp., and InTouchCX.

What specific sectors is the Philippines targeting for growth?
The focus is on expanding capabilities in artificial intelligence (AI), software engineering, digital innovation, cybersecurity, and healthcare information management.

How do you think this shift toward AI and digital services will impact the future of the local workforce?

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Tech

Hardwood: High-Speed JVM Apache Parquet Processing Without Dependencies

by Chief Editor
written by Chief Editor

Hardwood, an open-source library for the Java Virtual Machine (JVM), has reached version 1.0, offering a high-performance, near zero-dependency alternative for reading Apache Parquet files. Initiated by Gunnar Morling, the project utilizes multi-threaded page decoding to maximize CPU utilization, achieving throughputs of 16.5 million rows per second on 8 vCPUs.

How Hardwood Improves Parquet Performance

Traditional Apache Parquet implementations for Java often rely on single-threaded core readers and carry significant dependency overhead. According to project documentation, Hardwood bypasses these limitations by employing a multi-threaded approach that distributes page decoding across all available CPU cores. This architecture reduces the latency inherent in sequential processing, allowing the library to better saturate system I/O and CPU bandwidth.

The library provides two distinct APIs to balance engineering needs: a structured row reader API for general record access and a batch-oriented column reader API for high-throughput analytical tasks. Furthermore, the library implements branchless, batch-at-a-time predicate evaluation during filtered scans, which minimizes CPU branch mispredictions—a common performance bottleneck in analytical data processing.

Pro Tip: When working with high-throughput analytical workloads, leverage the batch-oriented column reader API to minimize overhead and maximize the efficiency of your CPU resources.

Why Zero-Dependency Design Matters

Hardwood is built with a zero-mandatory-dependency profile to mitigate risks associated with supply chain attacks and classpath conflicts. By utilizing Java’s minimal logging abstraction, which has been available since version 9, the library avoids external logging dependencies entirely. Developers can opt into additional functionality—such as LZ4 or GZip compression and S3 object storage support—by pulling in specific optional dependencies only when necessary.

Gunnar Morling Built a New Parquet Engine with AI | Ep. 31 | Confluent Developer Podcast

This modular approach contrasts with older, monolithic Java data libraries that often force developers to include large, unnecessary dependency trees. The inclusion of a command-line interface (CLI) with a text-based user interface (TUI) further reduces the need for heavy frameworks, allowing engineers to inspect file schemas and verify data integrity directly from the terminal.

What Lies Ahead for the Project

Since its inception in early 2026, Hardwood has grown to include 20 contributors, including Andres Almiray and Bruno Borges. While version 1.0 currently focuses on read capabilities, the project roadmap explicitly lists write support as a future priority. This addition is highly anticipated by the community, as indicated by feedback from early users.

Did you know? Despite the heavy reliance on complex algorithms for Parquet decoding, the project utilized AI-assisted coding during its development phase, though the critical design and code review processes remained under human ownership.

Frequently Asked Questions

  • What is the primary benefit of using Hardwood over standard Parquet implementations?
    Hardwood provides significantly higher throughput by utilizing multi-threaded page decoding and eliminates heavy dependency overhead, reducing both runtime latency and the risk of classpath conflicts.
  • Does Hardwood support writing Parquet files?
    Not yet. Version 1.0 is limited to read capabilities, but the project roadmap confirms that write support is planned for future releases.
  • Can I use Hardwood with AWS S3?
    Yes, Hardwood supports object storage services like S3 through optional dependencies that can be added to your project as needed.
  • Is the Hardwood CLI suitable for production environments?
    The CLI is primarily designed as a diagnostic tool for developers and data engineers to verify file structure and inspect metadata without the need for heavy processing frameworks.

Are you currently integrating Hardwood into your data pipelines? Share your performance benchmarks or questions in the comments section below to join the discussion on the future of high-performance JVM data processing.

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Entertainment

From Screen to Soil: How AI Cost a Chinese Actor His Career

by Chief Editor
written by Chief Editor

Why Are AI-Produced Dramas Reshaping the Entertainment Industry?

AI-generated content is rapidly transforming the entertainment sector, with a vast majority of short-form dramas in China produced by artificial intelligence as of Q1 2026, according to a report. This shift has left actors like Xu Peng, once a rising star in short-form dramas, facing sudden career disruptions.

What Happened to Xu Peng, the Actor Turned Vegetable Seller?

Xu Peng, a 30-year-old actor known for his role as an arrogant CEO in short-form dramas, transitioned from Hengdian’s film studios to a Shandong village market. After his last project in early 2026, he returned to his hometown to sell vegetables, a stark contrast to his previous life of 15 to 16-hour workdays. “Menjadi aktor hanyalah sebuah profesi,” he said, emphasizing his adaptability. “Jika tidak ada pekerjaan akting, aku akan mencari cara lain.”

How Is AI Impacting Employment in the Entertainment Sector?

China’s short-form drama industry, which exploded in popularity around 2025, now faces a crisis. A report revealed that 122,000 of 128,000 short dramas released in Q1 2026 were AI-generated, leaving human actors sidelined.

What Does This Mean for Actors and Creatives?

Xu Peng’s story highlights this challenge: his training at the Central Academy of Drama prepared him for traditional roles.

What Does This Mean for Actors and Creatives?

What Are the Broader Implications of AI in Content Creation?

The rise of AI-generated content raises ethical questions. Xu Peng, now a market vendor, remains optimistic. “Meskipun profesiku berubah, aku tetap orang yang sama,” he said, reflecting a mindset of resilience.

Did You Know?

What’s Next for the Entertainment Industry?

As AI capabilities grow, the line between human and machine-generated content will blur. However, Xu Peng’s experience underscores the urgency for industry-wide adaptation.

Pro Tips for Navigating the AI-Driven Entertainment Landscape

  • Diversify skills: Learn editing, scriptwriting, or AI tools to stay relevant.
  • Network with tech-savvy producers: Collaborate on hybrid projects that blend human and AI creativity.
  • Advocate for fair policies: Support initiatives that protect artists’ rights in the digital age.

Frequently Asked Questions

How many short-form dramas in China are now AI-generated?

According to a report, 122,000 of 128,000 short-form dramas released in Q1 2026 were AI-produced.

Frequently Asked Questions

What challenges do actors face due to AI adoption?

Actors like Xu Peng report sudden job loss.

Can actors adapt to AI-driven industries?

Some, like Xu Peng, are finding new roles outside acting. However, the pace of change leaves many struggling to keep up.

Call to Action

What’s your take on AI’s role in entertainment? Share your thoughts in the comments below or explore our latest coverage on the future of work and technology.

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Tech

AI Translates Protein Sequences Into Natural Language

by Chief Editor
written by Chief Editor

Researchers at the Israel Institute of Technology have developed an artificial intelligence system named “BetaDescribe” that translates complex protein sequences into natural-language descriptions. Published in the journal Proceedings of the National Academy of Sciences, the tool is expected to significantly accelerate medical research and reduce costs in drug discovery and biotechnology.

How does BetaDescribe accelerate drug discovery?

BetaDescribe functions as a specialized translator, turning raw biological data into clear, detailed descriptions of a protein’s function, metabolic role, and medical potential. According to the Israel Institute of Technology, while nature contains billions of proteins, scientists have identified the functions of only a small fraction, largely due to years of costly lab work. By generating evidence-based hypotheses rapidly, the AI could significantly shorten the path from basic discovery to medical and industrial applications.

Did you know?
Proteins are essential to biological functions and underpin medical advances such as the diabetes drug Ozempic.

Why is protein function identification a challenge?

Despite the existence of billions of proteins in nature, the scientific community has identified the functions of only a small fraction of them. As noted by the researchers, years of costly lab work are typically required. BetaDescribe addresses this by turning raw biological data into clear, detailed descriptions of a protein’s function, metabolic role, and medical potential.

Why is protein function identification a challenge?

What are the long-term implications for biotechnology?

The system’s ability to rapidly generate evidence-based hypotheses about the functions of unknown proteins could significantly shorten the path from basic discovery to medical and industrial applications, the researchers concluded.

Pro Tip: Staying updated on AI in biology

To follow the progress of tools like BetaDescribe, monitor publications in the Proceedings of the National Academy of Sciences.

Frequently Asked Questions

What is BetaDescribe?
It is an AI system developed by the Israel Institute of Technology that translates complex protein sequences into readable natural-language descriptions.

Why is this technology important for medicine?
It is expected to significantly accelerate medical research and reduce costs in drug discovery and biotechnology.

How does it differ from traditional methods?
Traditional methods involve years of costly lab work, whereas BetaDescribe works like a specialized translator, turning raw biological data into clear, detailed descriptions.

Are you interested in the intersection of AI and healthcare? Subscribe to our newsletter for the latest updates on biotech breakthroughs or join the conversation in the comments section below.

T³ – Technology Transfer from Technion – Israel Institute of Technology
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Tech

What DSP Adoption Reveals About Edge AI

by Chief Editor
written by Chief Editor

Edge AI is currently hitting an adoption ceiling similar to the trajectory of Digital Signal Processors (DSPs) in the 1990s, where high-performance hardware is being bottlenecked by fragmented software ecosystems and complex developer workflows. According to Synaptics, the shift from innovation to large-scale deployment depends less on raw silicon power and more on the maturity of compilers, toolchains, and abstraction layers that allow embedded developers to integrate accelerators into heterogeneous systems.

Why does hardware performance fail to guarantee market success?

Technical innovation in silicon rarely translates into market dominance without a corresponding software ecosystem. During the 1990s, DSPs offered significant power and performance benefits for signal-heavy tasks like image processing and communications, yet they remained difficult to adopt. Synaptics notes that the hurdle was not the hardware itself but the lack of mature software tooling and compiler support.

Why does hardware performance fail to guarantee market success?

Today’s Neural Processing Units (NPUs) face the same reality. While these accelerators provide superior throughput and latency, they often require developers to master proprietary tools or perform manual, low-level optimizations. A high-performance chip is merely a component; it only becomes a platform when developers can reliably integrate it into production software without excessive custom rework.

Pro Tip: Focus on toolchain compatibility early in the hardware selection process. If your team spends more time writing custom kernels than deploying models, your hardware choice may be hindering your scaling potential.

How can abstraction solve the heterogeneity problem?

Modern edge devices rarely rely on a single processor. They typically utilize a mix of CPUs, DSPs, NPUs, and fixed-function accelerators. This heterogeneity is where many edge AI projects stall. Synaptics highlights that if every compute element requires an isolated compiler model, the development burden grows faster than the performance gains.

How can abstraction solve the heterogeneity problem?

The industry is looking to frameworks like MLIR (Multi-Level Intermediate Representation) to bridge this gap. By representing workloads at an appropriate level, software can map tasks cleanly onto diverse compute resources. The goal is to move away from hand-tuned, architecture-specific code toward a model where hardware is designed to align with existing compiler infrastructure, such as the foundations laid by LLVM.

What role does ecosystem maturity play in scaling?

Market breadth is defined by the “everything else” factor: documentation, libraries, model portability, and lifecycle support. Synaptics identifies three pillars necessary for edge AI to reach mainstream adoption:

Simplifying On-Device Edge AI with the Synaptics Coral Dev Board
  • Openness: Toolchains must evolve alongside AI research; vendor-specific stacks that rely on fixed assumptions quickly become obsolete.
  • Portability: Developers need to transition across compute architectures without rebuilding their entire workflow for every new accelerator.
  • Lifecycle support: Edge devices often remain in the field for years. Ecosystems must handle software updates and model improvements without requiring a complete hardware overhaul.
Did you know? The success of the GCC compiler framework proved that a single, reusable toolchain could support multiple architectures, a lesson now being applied to heterogeneous AI compute.

Frequently Asked Questions

Why is edge AI harder to deploy than general-purpose computing?
Edge systems operate under strict constraints including thermal budgets, power limitations, memory availability, and long-term lifecycle requirements that general-purpose hardware does not have to manage.
What is the biggest risk for companies investing in proprietary NPU tools?
The primary risk is “vendor lock-in” combined with rapid software obsolescence. Proprietary stacks often fail to keep pace with the speed of AI research, leaving teams with hardware that cannot run modern models.
How do I ensure my edge AI project can scale?
Prioritize platforms that offer open tools and heterogeneous support. Scalability comes from usability—the ease with which your engineering team can target, optimize, and maintain compute resources over time.

Are you struggling with the transition from prototype to production? Join the conversation below or explore our developer resources to learn how to streamline your edge AI deployment strategy.

Frequently Asked Questions
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Business

Oracle’s Data Center Warning: Is the AI Boom at Risk?

by Chief Editor
written by Chief Editor

Oracle has officially warned investors that its aggressive transition into an artificial intelligence infrastructure provider carries significant financial and operational risks. According to the company’s June annual report, the tech giant faces potential hurdles including supply chain bottlenecks, rising energy costs, and the creditworthiness of its AI-focused customers. Despite these challenges, Oracle plans to increase capital expenditures to as much as $95 billion for fiscal 2027 to meet surging demand for computing capacity.

Why is Oracle increasing its AI infrastructure spending?

Oracle is scaling its operations to support massive data center buildouts required for training and deploying advanced AI models. In its annual filing, the company noted that to grow its Oracle Cloud Infrastructure (OCI) business, it must incur significant capital and operating expenditures. This commitment is reflected in the company’s fiscal data: capital expenditures rose to $55.7 billion in fiscal year 2026, up from $21.2 billion the previous year.

The company has secured major contracts with firms like OpenAI and Meta, necessitating a rapid expansion of power-hungry data centers. Furthermore, Oracle founder Larry Ellison joined OpenAI CEO Sam Altman and SoftBank CEO Masayoshi Son at a White House event to announce “Stargate,” a long-term infrastructure project that could involve up to $500 billion in investment.

Did you know?
At the Stargate project announcement, OpenAI CEO Sam Altman described the initiative as “the most important project of this era,” citing its potential to help discover cures for diseases like cancer.

What are the primary risks to Oracle’s AI bet?

Oracle’s annual report explicitly details several factors that could derail its growth, including the possibility that customers may struggle to pay for services. The company stated that some of its clients are highly leveraged and subject to their own regulatory risks, which could lead to non-payment or non-performance. This concern is particularly relevant as prominent AI firms, such as OpenAI and Anthropic, continue to operate with high burn rates.

Beyond financial stability, the company identified several external threats:

  • Regulatory Scrutiny: Governments are increasingly focusing on the environmental impact and energy consumption of data centers.
  • Supply Chain Constraints: Building the necessary capacity is subject to delays outside of Oracle’s direct control.
  • Cybersecurity: Increased infrastructure complexity brings heightened risks regarding data protection and system integrity.

How does Oracle compare to other tech firms?

While many companies disclose business risks, Oracle’s filing is notable for its granular detail regarding the technical and financial hurdles of the AI buildout. For example, while SpaceX disclosed in its own filing that Grok’s controversial features could pose a reputational risk, Oracle’s report provides a comprehensive overview of the systemic risks facing the entire AI industry.

Larry Ellison shares some details on the new “Stargate” AI infrastructure project

Recent market performance highlights growing investor caution. Oracle shares have fallen 40% over the past month, a trend mirrored by other major players. Nvidia shares have also tumbled during this period, and SpaceX has seen its stock price struggle to move significantly above its $150 opening price.

Pro Tip: When evaluating AI stocks, look beyond the revenue growth figures. Industry reports like those from Oracle provide a “cheat sheet” on the underlying costs—such as energy and hardware—that dictate long-term profitability.

Frequently Asked Questions

What is the Stargate project?

Stargate is a proposed massive AI infrastructure project involving Oracle, OpenAI, and SoftBank. It aims to invest up to $500 billion in data center capacity over the coming years.

Frequently Asked Questions

Why are Oracle’s shares falling?

Shares have trended downward amid broader investor caution regarding the massive capital expenditures required to sustain the current AI boom and the long-term profitability of major AI customers.

What are the primary costs for AI data centers?

According to Oracle’s filings, the primary drivers of rising costs include significant capital expenditures for computing hardware and soaring energy requirements to power and cool advanced AI models.

Are you tracking the impact of AI infrastructure on the tech sector? Subscribe to our newsletter for the latest updates on industry shifts and market analysis.

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Tech

Sandisk High Bandwidth Flash: Tackling the AI Memory Wall

by Chief Editor
written by Chief Editor

High Bandwidth Flash (HBF): The New Frontier in AI Memory

As artificial intelligence models grow in size and complexity, the hardware required to run them is hitting a critical bottleneck: memory. While High Bandwidth Memory (HBM) provides the speed necessary for training, its limited capacity and high cost make it difficult to scale for massive inference workloads.

Enter High Bandwidth Flash (HBF). This emerging technology aims to bridge the gap between the extreme speed of DRAM and the massive capacity of NAND flash, offering a memory tier specifically optimized for AI inference.

What is High Bandwidth Flash (HBF)?

HBF is a specialized memory architecture that leverages advanced NAND flash technology to provide significantly higher bandwidth than traditional storage, while maintaining a capacity far greater than HBM. By optimizing the interface and controller, HBF allows AI accelerators to stream massive model weights and KV caches more efficiently.

The performance leap is substantial. According to Sandisk, HBF can achieve the following throughput:

  • First Generation: 1.2 TB/s
  • Second Generation: 2 TB/s
  • Third Generation: 3.2 TB/s

In terms of capacity, HBF offers a dramatic increase over HBM:

  • First Generation: 1 TB
  • Second Generation: 1 TB
  • Third Generation: 1.5 TB

While HBF has higher latency than DRAM, Sandisk simulations show that when reading pretrained weights for a Llama 3.1 405B model, HBF performed within 2.2% of a hypothetical unlimited-capacity HBM setup. This makes it highly effective for streaming large model weights.

Who is driving the standardization of HBF?

Who is driving the standardization of HBF?

The move toward HBF is gaining momentum through high-level industry partnerships. In February 2026, Sandisk and SK hynix began global standardization efforts for HBF at Sandisk’s Milpitas headquarters, working through the Open Compute Project.

The HBF technical advisory board is chaired by David Patterson, a University of California, Berkeley emeritus professor and Google distinguished engineer. Patterson, a 2017 ACM Turing Award winner, stated that HBF could drive down costs for AI applications that are currently unaffordable by delivering unprecedented capacity at high bandwidth.

The advisory board also includes Raja Koduri, founder and CEO of Oxmiq Labs and former AMD chief architect. Koduri suggests that HBF will revolutionize edge AI by allowing sophisticated models to run locally in real time.

The involvement of SK hynix is particularly notable. Despite holding roughly 62% of the HBM market, SK hynix is collaborating on the HBF specification. SK hynix President and Chief Development Officer Ahn Hyun framed this as an ecosystem optimization strategy rather than a competition between individual technologies.

What are the implications for edge AI and enterprises?

What Is HBF? The Companies Tied to the Next AI Memory Trade

HBF’s ability to maintain data when power is lost (persistence) and its stability at high operating temperatures offer unique advantages for edge computing. Ilkbahar suggests this could allow smartphones to make real-time decisions and seamlessly retrieve old context from previous queries without needing to communicate with the cloud.

For the enterprise sector, HBF could democratize access to high-level AI. Because HBF-enabled accelerators are expected to be more cost-effective, smaller companies may finally have the resources to fine-tune large, pre-trained models for domain-specific uses, a task previously reserved for hyperscale providers.

Pro Tip: When evaluating AI infrastructure, focus on the “inference-to-training” ratio. As models move from development to deployment, memory capacity and cost-per-terabyte become more critical than raw DRAM latency.

How will HBF impact the Australian data center market?

How will HBF impact the Australian data center market?

The shift toward more efficient memory tiers arrives as Australia experiences a massive influx of data center investment. Amazon has committed A$20 billion to Australian data centers between 2025 and 2029, marking one of the largest tech investments in the country’s history.

Additionally, OpenAI and NextDC are developing an A$7 billion AI campus in Sydney. With the local hyperscale market projected to grow from $6.27 billion in 2026 to $16.18 billion by 2031, the ability to manage power and cost via technologies like HBF will be essential for the economic viability of these large-scale clusters.

Frequently Asked Questions

Is High Bandwidth Flash (HBF) a replacement for DRAM?
No. HBF is aimed at AI inference workloads where high capacity and bandwidth are needed, but extreme low latency is less critical. It is intended to complement rather than replace DRAM.

When will HBF be available for commercial use?
Sandisk expects first samples to arrive in the second half of 2026, with the first AI-inference devices utilizing HBF expected to hit the market in early 2027.

Why is SK hynix supporting a technology that competes with its HBM business?
SK hynix views the move as an ecosystem play. By helping define the HBF standard, they aim to optimize the entire AI infrastructure rather than focusing solely on the performance competition of individual technologies.

What is the main benefit of HBF for large language models (LLMs)?
HBF supports large KV caches, which allows chatbots to handle long, complex user prompts and maintain context over many messages more efficiently.

Want to stay updated on the latest in AI hardware and semiconductor trends? Subscribe to our newsletter or leave a comment below with your thoughts on the future of memory architecture.

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Tech

Microsoft Planning Thousands of New Job Cuts

by Chief Editor
written by Chief Editor

Microsoft is preparing to announce job cuts next week that will impact thousands of employees across its sales, consulting, and Xbox divisions. According to people familiar with the situation, the layoffs will affect less than 2.5% of the company’s 220,000-person workforce as the tech giant seeks to control costs while increasing spending on artificial intelligence.

Why is Microsoft reducing its workforce now?

The planned reductions are part of a strategic move to reallocate capital toward artificial intelligence development. Microsoft is facing pressure from Wall Street regarding the potential for AI to replace existing software services, including some of its own core offerings. This investor concern has contributed to a roughly 17% slump in the company’s stock over the past month.

To manage these transitions, Microsoft has previously used voluntary programs to reduce headcount. Earlier this year, the company offered a voluntary retirement program to US-based employees at level 67 and below who met specific age and service requirements. An internal document viewed by Business Insider showed that sales employees receiving commission-based compensation were excluded from that specific buyout offer.

Did you know?

About one-third of the 9,000 US employees eligible for Microsoft’s recent voluntary retirement program chose to take the buyout, which helped the company maintain a lower total layoff percentage compared to the previous year.

How do these cuts compare to previous Microsoft layoffs?

The current plan to cut less than 2.5% of the workforce contrasts with the scale of layoffs seen last year.

How do these cuts compare to previous Microsoft layoffs?
Period Approximate Roles Cut Percentage of Workforce
May (Last Year) 6,000 Not specified
July (Last Year) 9,000 ~4%
Upcoming Round Thousands < 2.5%

According to the people familiar with the matter, some affected employees will be offered new roles within the company immediately following the announcement.

What is happening within the Xbox gaming division?

The Xbox division is among the departments targeted in the upcoming cuts. Reductions in the gaming sector have been anticipated following a memo from Asha Sharma, which called for a “reset” of the business unit.

Frequently Asked Questions

When will Microsoft announce the layoffs?

The company is planning to announce the layoffs next week, though the exact timing may change, according to people familiar with the situation.

Big AI, Big Layoffs: Microsoft cuts 4% of workforce

Which departments are being affected?

The cuts are expected to impact roles in sales, consulting, and the Xbox gaming division.

Why is Microsoft cutting jobs if they are profitable?

The cuts are intended to control costs and allow the company to increase spending on artificial intelligence to meet Wall Street expectations and technological shifts.

Stay informed on the latest shifts in the technology sector. Share your thoughts on these industry trends in the comments below or subscribe to our newsletter for daily updates.

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Tech

Microsoft Copilot Autofix: AI-Powered Vulnerability Remediation for Azure DevOps

by Chief Editor
written by Chief Editor

Microsoft has launched a limited public preview of Copilot Autofix for GitHub Advanced Security within Azure DevOps, allowing teams to automatically detect and remediate software vulnerabilities. By integrating static analysis from CodeQL with generative AI, the platform creates pull requests that suggest code fixes for developer review. This expansion aims to shorten the time between vulnerability identification and resolution while maintaining human oversight in existing workflows.

How does Copilot Autofix integrate with Azure DevOps?

The new functionality brings AI-driven remediation to organizations that rely on Azure Repos rather than GitHub repositories. According to Microsoft, the tool functions by pairing the deep semantic analysis of CodeQL with the coding agent capabilities of GitHub Copilot. When CodeQL identifies a supported security alert, the platform analyzes the vulnerability within the context of the surrounding application. It then generates a proposed code change and opens a pull request, which developers must review, test, and approve before it is merged into the codebase.

How does Copilot Autofix integrate with Azure DevOps?
Did you know?
Microsoft’s move into AI-assisted remediation is part of a broader strategy to bridge the feature gap between GitHub and Azure DevOps. Previous integrations have already brought CodeQL default setup and secret scanning to Azure Repos.

Why is AI-assisted remediation becoming an industry standard?

Security teams face a growing bottleneck in the “last mile” of software delivery: the time spent interpreting alerts and manually writing patches. Static application security testing (SAST) tools have historically excelled at finding risks but often provided little help in the actual repair process. By automating the creation of candidate fixes, platforms like Copilot Autofix—alongside similar offerings from GitLab, Snyk, Sonar, and Checkmarx—aim to keep pace with the rapid volume of code generation driven by modern AI tools.

What are the risks of using AI for security fixes?

While AI can accelerate maintenance, Microsoft warns that generated fixes are not guaranteed to be complete or free from unintended side effects. Research into agent-generated pull requests indicates that many AI-proposed fixes are ultimately rejected due to incorrect assumptions or failures during CI validation. Because of these challenges, Microsoft maintains that developers remain responsible for the final code. The system does not operate autonomously; it functions as an assistant that respects existing governance and quality assurance practices.

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Comparison: Traditional vs. AI-Assisted Remediation

Comparison: Traditional vs. AI-Assisted Remediation
Feature Traditional SAST Copilot Autofix
Detection Manual analysis required Context-aware analysis
Remediation Manual coding AI-generated PRs
Oversight Full manual review Human-in-the-loop review
Pro Tip:
Even when using AI to generate fixes, treat every pull request as if it were written by a junior developer. Always run your full suite of unit and integration tests before merging to ensure the AI hasn’t introduced regression errors.

Frequently Asked Questions

  • Does Copilot Autofix replace human security engineers? No. Microsoft emphasizes that developers must validate every fix, as the AI is an assistant rather than an autonomous replacement.
  • Is this feature available for all repositories? The current limited public preview is specifically designed for GitHub Advanced Security for Azure DevOps users.
  • Does the tool only fix single lines of code? No. The platform is capable of proposing coordinated changes across multiple files to resolve complex issues correctly.

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